Applications of Artificial Intelligence in Training and Tutoring
A number of academic and industrial researchers have used artificial intelligence in an effort to teach a variety of subjects including geometry, computer programming languages, medical diagnosis and electronic trouble shooting. The earliest published reports which suggested the application of artificial intelligence concepts to teaching tasks appeared in the early 1970's. The article entitled "AI in CAI: An artificial Intelligence Approach to CAI" by J. R. Carbonell in the IEEE Transactions on Machine Systems, Vol. 11, No. 4, p. 190 (1970) and the article entitled "Towards Intelligent Teaching Systems" by J. R. Hartley and D. H. Sleeman in the International Journal of Machine Studies, Vol. 5, p. 215, (1973) are of specific interest. Hartley and Sleeman proposed an architecture for an intelligent tutoring system. However, since such proposal, no agreement has been reached among researchers on a general architecture for intelligent tutoring systems.
Examples of intelligent tutoring systems are SOPHIE (Brown, Burton & de Kleer, 1972, "Pedagogical, Natural Language and Knowledge Engineering Techniques in SOPHIE I, II, and III"; D. Sleeman & J. S. Brown (Eds.), Intelligent Tutoring Systems (p. 227). London: Academic Press), PROUST (Johnson & Soloway, April 1985, PROUST, Byte, Vol. 10, No. 4, p. 179) and LISP Tutor (Anderson & Reiser, April 1985, "The LISP Tutor," Byte, Vol. 10, No. 4, p. 159). SOPHIE was one of the first artificial intelligence ("AI") systems that was developed. SOPHIE was developed in response to a U.S. Air Force interest in a computer-based training course in electronic trouble shooting. SOPHIE contains three major components: an electronics expert with a general knowledge of electronic circuits, together with detailed knowledge about a particular type of circuit; a coach which examines student inputs and decides if it is appropriate to stop the student and offer advice; and a trouble shooting expert that uses the electronics expert to determine which possible measurements are most useful in a particular context. Although three versions of SOPHIE were produced, SOPHIE was never viewed as a finished product. One of the major problems associated with the SOPHIE systems was the lack of a user model.
PROUST and the LISP Tutor are two well-known, intelligent teaching systems that have left the laboratory for general application. PROUST, and its related program MICRO-PROUST, is a "debugger" for finding nonsyntactical errors in Pascal programs written by student programers. The developers of PROUST claim that it is capable of finding all of the bugs in at least 70% of the "moderately complex" programming assignments that it examines. PROUST contains an expert Pascal programer that can write "good" programs for the assignments given to students. Bugs are found by matching the assertions of the expert program with that of the student; mismatches are identified as "bugs" in the student program. After finding a bug, PROUST provides an English language description of the bug to the student, enabling the student to correct his or her error. PROUST cannot handle student programs that depart radically from the programming "style" of the expert.
The LISP Tutor is used to teach the introductory LISP course offered at Carnegie-Mellon University. The LISP Tutor system is based on the ACT (Adaptive Control of Thought) theory and consists of four elements: a structured editor which serves as an interface to the system for students, an expert LISP programmer that provides an "ideal" solution to a programming problem, a bug catalog that contains errors made by novice programmers, and a tutoring component that provides both immediate feedback and guidance to the student. Evaluations of the LISP Tutor show that it can achieve results similar to those obtained by human tutors. One of the LISP Tutor's primary features is its enforcement of what its authors regard as a "good" programming style. The "good" programming style feature prevents creative authorship by the student.
The existing systems are "intelligent tutoring or teaching systems." The teaching/tutoring task is distinguished from the training task. The training environment differs in many ways from an academic teaching environment. The differences are important in the design of an architecture for an intelligent training system. For example, assigned tasks are often mission-critical, i.e., the responsibility for lives and property depends on how well a person is trained to perform a task. Typically, people who are being trained already have significant academic and practical experience which is utilized in the task they are being trained to do. Also, trainees make use of a wide variety of training techniques. Different training techniques can range from the study of comprehensive training manuals, to simulations, to actual on-the-job training under the supervision of more experienced, trained personnel. Few tasks which require training must be accomplished by one method or style as exists in typical tutoring. Training a person to perform a task may require that considerable freedom be given the trainee in the exact manner in which the task may be accomplished.
People being trained for complex, mission-critical tasks are usually already highly motivated. Training for such complex tasks imposes on the trainer the responsibility for the accuracy of the training content and the ability of the trainer to correctly evaluate trainee actions. Typical tutoring systems do not provide such flexibility. A training system is intended to aid the trainee in developing skills for which he already has the basic or "theoretical" knowledge. A training system is not intended to impart basic knowledge such as mathematics or physics. Simply stated, a true training system is designed to help a trainee put into practice that which he already intellectually understands. Most importantly, a trainee must be allowed to perform an assigned task by any valid means. To achieve meaningful training, the flexibility to carry out any assigned task by any valid means is essential. Trainees must be able to retain and even hone an independence of thought and develop confidence in their ability to respond to problems, including problems which the trainee has never encountered and which the trainer may have never anticipated.
All phases of industry and government must maintain a large effort in training personnel. New personnel must be trained to perform the task which they are hired to perform, continuing personnel must be trained to upgrade or update their ability to perform assigned tasks and continuing personnel must be trained to perform new tasks. Often a great number of training methodologies are employed, singly or in concert. These methods include training manuals, formal classes, procedural computer programs, simulations, and on-the-job training. The latter method is particularly effective in complex tasks where a great deal of independence is granted to the task performer. Of course, on-the-job training is typically the most expensive and may be the most impractical training method, especially where there are many trainees and few experienced personnel to conduct such training.